Xu Chunyu, Li Na, Yang Yibing, Li Yunpu, Liu Zhe, Wang Qin, Zheng Tongzhang, Civitarese Anna, Xu Dongqun
a National Institute of Environmental Health , Chinese Center for Disease Control and Prevention , Chaoyang District, Beijing , People's Republic of China.
b School of Public Health , Brown University , Providence , RI , USA.
J Air Waste Manag Assoc. 2017 Jun;67(6):694-701. doi: 10.1080/10962247.2016.1272503. Epub 2016 Dec 23.
The objective of this study was to estimate the residential infiltration factor (Finf) of fine particulate matter (PM) and to develop models to predict PM Finf in Beijing. Eighty-eight paired indoor-outdoor PM samples were collected by Teflon filters for seven consecutive days during both non-heating and heating seasons (from a total of 55 families between August, 2013 and February, 2014). The mass concentrations of PM were measured by gravimetric method, and elemental concentrations of sulfur in filter deposits were determined by energy-dispersive x-ray fluorescence (ED-XRF) spectrometry. PM Finf was estimated as the indoor/outdoor sulfur ratio. Multiple linear regression was used to construct Finf predicting models. The residential PM Finf in non-heating season (0.70 ± 0.21, median = 0.78, n = 43) was significantly greater than in heating season (0.54 ± 0.18, median = 0.52, n = 45, p < 0.001). Outdoor temperature, window width, frequency of window opening, and air conditioner use were the most important predictors during non-heating season, which could explain 57% variations across residences, while the outdoor temperature was the only predictor identified in heating season, which could explain 18% variations across residences. The substantial variations of PM Finf between seasons and among residences found in this study highlight the importance of incorporating Finf into exposure assessment in epidemiological studies of air pollution and human health in Beijing. The Finf predicting models developed in this study hold promise for incorporating PM Finf into large epidemiology studies, thereby reducing exposure misclassification.
Failure to consider the differences between indoor and outdoor PM may contribute to exposure misclassification in epidemiological studies estimating exposure from a central site measurement. This study was conducted in Beijing to investigate residential PM infiltration factor and to develop a localized predictive model in both nonheating and heating seasons. High variations of PM infiltration factor between the two seasons and across homes within each season were found, highlighting the importance of including infiltration factor in the assessment of exposure to PM of outdoor origin in epidemiological studies. Localized predictive models for PM infiltration factor were also developed.
本研究的目的是估算细颗粒物（PM）的住宅渗透因子（Finf），并建立预测北京PM Finf的模型。在非供暖季和供暖季期间，连续7天用特氟龙滤膜采集了88对室内外PM样本（来自2013年8月至2014年2月期间的55个家庭）。通过重量法测量PM的质量浓度，并用能量色散X射线荧光光谱法（ED-XRF）测定滤膜沉积物中硫的元素浓度。PM Finf通过室内/室外硫比率估算。采用多元线性回归构建Finf预测模型。非供暖季的住宅PM Finf（平均值为0.70±0.21，中位数为0.78，n = 43）显著高于供暖季（平均值为0.54±0.18，中位数为0.52，n = 45，p < 0.001）。室外温度、窗户宽度、开窗频率和空调使用是非供暖季最重要的预测因素，可解释不同住宅间57%的差异，而供暖季唯一确定的预测因素是室外温度，可解释不同住宅间18%的差异。本研究中发现的PM Finf在季节间和住宅间的显著差异凸显了在北京市空气污染与人类健康的流行病学研究中将Finf纳入暴露评估的重要性。本研究开发的Finf预测模型有望将PM Finf纳入大型流行病学研究，从而减少暴露错误分类。
在通过中心站点测量估算暴露的流行病学研究中，未考虑室内外PM的差异可能导致暴露错误分类。本研究在北京开展，旨在调查住宅PM渗透因子，并在非供暖季和供暖季建立本地化预测模型。研究发现两个季节之间以及每个季节内不同家庭的PM渗透因子差异很大（此处“高变化”表述有误，应为“差异很大”），凸显了在流行病学研究中评估来自室外源PM暴露时纳入渗透因子的重要性。还开发了PM渗透因子的本地化预测模型。
